Water quality sensors are often spatially distributed in water distribution systems (WDSs) to detect contamination events and monitor quality parameters (e.g., chlorine residual levels), thereby ensuring safety of a WDS. The performance of a water quality sensor placement strategy (WQSPS) is not only affected by sensor spatial deployment that has been extensively analyzed in literature, but also by possible sensor failures that have been rarely explored so far. However, enumerating all possible sensor failure scenarios is computationally infeasible for a WQSPS with a large number of sensors. To this end, this paper proposes an evolutionary algorithm (EA) based method to systematically and efficiently investigate the WQSPS′ global resilience considering all likely sensor failures. First, new metrics are developed in the proposed method to assess the global resilience of a WQSPS. This is followed by a proposal of an efficient optimization approach based on an EA to identify the values of global resilience metrics. Finally, the sensors within the WQSPS are ranked to identify their relative importance in maintaining the WQSPS's detection performance. Two real-world WDSs with four WQSPSs for each case study are used to demonstrate the utility of the proposed method. Results show that: (i) compared to the traditional global resilience analysis method, the proposed EA-based approach identifies improved values of global resilience metrics, (ii) the WQSPSs that deploy sensors close to large demand users are overall more resilient in handling sensor failures relative to other design solutions, thus offering important insight to facilitate the selection of WQSPSs, and (iii) sensor rankings based on the global resilience can identify those sensors whose failure would significantly reduce the WQSPS's performance thereby providing guidance to enable effective water quality sensor management and maintenance.

水质传感器通常在空间上分布在水分配系统（WDS）中，以检测污染事件并监视质量参数（例如，氯残留水平），从而确保WDS的安全性。水质传感器放置策略（WQSPS）的性能不仅受到已在文献中进行了广泛分析的传感器空间部署的影响，而且还受到迄今为止很少探讨的可能的传感器故障的影响。但是，对于具有大量传感器的WQSPS，枚举所有可能的传感器故障情况在计算上是不可行的。为此，本文提出了一种基于进化算法（EA）的方法，系统地，有效地研究了WQSPS的全局弹性，并考虑了所有可能的传感器故障。第一，建议的方法中将开发新的指标，以评估WQSPS的全局弹性。随后提出了一种基于EA的高效优化方法的建议，以识别全局弹性指标的值。最后，对WQSPS中的传感器进行排名，以确定它们在维持WQSPS的检测性能方面的相对重要性。每个案例研究使用两个带有四个WQSPS的实际WDS来演示所提出方法的实用性。结果表明：（i）与传统的全局弹性分析方法相比，基于EA的提议方法可确定全局弹性指标的改进值；（ii）在接近大需求用户的位置部署传感器的WQSPS在处理传感器方面总体上更具弹性相对于其他设计解决方案的失败，